论文标题
泰勒遗传编程用于符号回归
Taylor Genetic Programming for Symbolic Regression
论文作者
论文摘要
遗传编程（GP）是解决符号回归（SR）问题的常用方法。与依赖于预定义模型和解决SR问题的培训数据集的机器学习或深度学习方法相比，GP更专注于在搜索空间中找到解决方案。尽管GP在大规模的基准上具有良好的性能，但它随机将个体转换为搜索结果而无需利用数据集的特征。因此，GP的搜索过程通常很慢，最终结果可能是不稳定的。为了通过这些特征指导GP，我们为SR提出了一种新方法，称为Taylor Genetic编程（Taylorgp）（Taylorgp）（https：//kgae-cup.github.github.github.io/taylorgp/）。 TaylorGP利用Taylor多项式来近似适合数据集的符号方程。它还利用泰勒多项式提取符号方程的特征：低阶多项式歧视，可变性，边界，单调和奇偶校验。这些泰勒多项式技术增强了GP。实验是在三种基准的三种基准上进行的：经典的SR，机器学习和物理学。实验结果表明，TaylorGP不仅具有比九种基线方法更高的精度，而且在寻找稳定的结果方面也更快。
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space. Although GP has good performance on large-scale benchmarks, it randomly transforms individuals to search results without taking advantage of the characteristics of the dataset. So, the search process of GP is usually slow, and the final results could be unstable.To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP) (Code and appendix at https://kgae-cup.github.io/TaylorGP/). TaylorGP leverages a Taylor polynomial to approximate the symbolic equation that fits the dataset. It also utilizes the Taylor polynomial to extract the features of the symbolic equation: low order polynomial discrimination, variable separability, boundary, monotonic, and parity. GP is enhanced by these Taylor polynomial techniques. Experiments are conducted on three kinds of benchmarks: classical SR, machine learning, and physics. The experimental results show that TaylorGP not only has higher accuracy than the nine baseline methods, but also is faster in finding stable results.
